
Surface Electromyogram (sEMG)-based Hand Gesture Recognition (HGR) is regarded as a promising approach for a wide range of applications, including myoelectric control prosthesis [1]}, [2]}, [3]}, [4]}, virtual reality technologies [5]}, [6]}, Human Computer Interactions (HCI) [7]}, and rehabilitative gaming systems [8]}. sEMG signals contain electrical activities of the muscle fibers that can be employed to decode hand gestures and thereby enhance immersive HMI wearable systems for immersion in Mixed Reality (MR) environments [9]}, [10]}. Consequently, there has been a surge of interest in the development of Deep Neural Networks (DNNs) and Machine Learning (ML) models to identify hand gestures using sEMG signals. Generally speaking, sEMG datasets can be collected based on “sparse multichannel sEMG” or “High-Density sEMG (HD-sEMG)”. Despite advantages of HD-sEMG, its utilization leads to structural complexity [11]}, [12]}, while adoption of sparse multichannel sEMG signals requires fewer electrodes making it the common modality of choice for incorporation into wearable devices. Therefore, development of DNNs based on sparse sEMG signals has gained significant recent importance.
